Skip to content

Using Lumen AI

Once you're set up and exploring, here's how to get the most out of Lumen AI.

Message examples

Here are some ideas to spark your exploration:

Basic queries:

  • "What datasets are available?"
  • "Give me a summary of the dataset."
  • "What are the columns in the dataset?"
  • "What is the distribution of the 'species' column?"

Visualizations:

  • "Show me a scatter plot of 'flipper_length_mm' vs 'body_mass_g'."
  • "Create a histogram of 'bill_length_mm'."
  • "Show me a bar chart of average values by species."

Complex queries:

  • "Group by 'species' and show me the average 'flipper_length_mm'. Then plot a bar chart of the result."
  • "Filter the dataset for species 'Chinstrap' and calculate the median 'body_mass_g'. Then display and discuss the result."
  • "Create a histogram of 'bill_length_mm' and a box plot of 'flipper_length_mm' side by side."
  • "Add a new column calculating the ratio of 'flipper_length_mm' to 'body_mass_g'. Then plot a scatter plot of this ratio against 'bill_length_mm'."
  • "Select records where 'body_mass_g' is greater than 3500 and 'flipper_length_mm' is less than 200. Then provide summary statistics for these filtered records."

Get inspired:

  • "What could be interesting to explore?"

Combine multiple requests

You can ask the AI to perform several steps in one message. This helps you build complex analyses without multiple back-and-forths. For example: "Filter the data, create a visualization, then summarize the findings."

Understand the AI's reasoning

If you want to see how the AI arrived at an answer, enable Chain of Thought in Settings (left sidebar). This will show the LLM's reasoning steps in expandable cards within the chat.

Refine results

If results aren't what you expected, you have several options:

Rerun the query — Click the Rerun button to re-execute the last query. This is useful if there was a temporary error or if you want to see if the AI produces different results.

Continue the conversation — Send a new message to refine or adjust the results. For example: "Can you make that chart show only the top 5 items?" or "Add a trend line to the visualization."

Add annotations — For visualizations, click the annotation button (💬 icon) to add highlights, callouts, or labels. For example: "Highlight the peak values" or "Mark outliers in red."

Manually edit — Directly edit the SQL query or visualization specification in the editor panel. This works if you're comfortable with SQL or need precise control over the output.

Use manual editing for small tweaks (like changing chart colors or sort order), and send a new message for bigger changes to the underlying query or analysis approach.

Explorations

An Exploration is a persistent, contextual workspace for working with a specific dataset. It is created when a SQL query is first generated and captures the full interaction state, including the conversation, analyses, visualizations, and other data artifacts. An Exploration evolves over time, supports multiple questions and operations on the same data, and can be revisited or exported as a coherent unit.

Explorations start from the global context (available sources and metadata). If a question is a follow-up, the new exploration is nested under the parent; if it is not, Lumen creates a new top-level exploration.

Use the navigation menu to move between explorations or nest follow-ups under the exploration they build on.

Report mode

Report mode shows all your analyses on one page. Switch to Report mode from the left sidebar to see your explorations organized into a structured report with collapsible sections.

What you can do:

  • Review everything — Scroll through all analyses in one place
  • Edit prompts — Click any task to modify its instructions and rerun
  • Customize agents — Edit underlying agent prompts to change behavior
  • Export all — Download everything as a single Jupyter notebook

Planned Feature: Report Templates

We're working on the ability to export your report structure as a reusable YAML configuration file. This will let you:

  • Build a skeleton through exploration: Ask questions naturally, and the system captures the underlying report structure (which agents, what order, what prompts).
  • Create templated reports: Export a "Q3 Customer Analysis" report, then next quarter reload it, change "Q3" to "Q4", and run—no re-prompting needed.
  • Share report structures: Export a config, share with colleagues, and they get the exact same report framework against their own data.
  • Iterate without re-discovery: Tweak the config (reorder sections, adjust prompts, swap one analysis for another) without starting from scratch.

The LLM does the hard work once during exploration, then gets out of the way for repeated execution.

Export results

Export your session as a Jupyter notebook so you can reproduce, share, or build on your work.

Export current exploration: Use Export Notebook in the navigation menu to download a notebook containing the current exploration's questions, queries, and visualizations.

Export all explorations: Switch to Report mode (via the left sidebar), then use Export Notebook to download everything as a single notebook.

The notebook includes:

  • Markdown cells with your questions and AI responses
  • Code cells with SQL queries and Lumen specifications
  • Visualizations as executable code

Next steps

  • Building Lumen Apps — Configure models, add source controls, and customize behavior
  • Agents — Learn about the different agent types
  • Reports — Build structured, reproducible analytical workflows